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<a href="_gaussian_rbf_kernel_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Radial Gaussian kernel</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * </span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \author      T.Glasmachers, O. Krause, M. Tuma</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \date        2010-2012</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * </span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * </span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * </span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * </span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> *</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> */</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#ifndef SHARK_MODELS_KERNELS_GAUSSIAN_RBF_KERNEL_H</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#define SHARK_MODELS_KERNELS_GAUSSIAN_RBF_KERNEL_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_kernel_function_8h.html" title="abstract super class of all kernel functions">shark/Models/Kernels/AbstractKernelFunction.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>{</div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment"></span> </div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">/// \brief Gaussian radial basis function kernel.</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment">///</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">/// Gaussian radial basis function kernel</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">/// \f$ k(x_1, x_2) = \exp(-\gamma \cdot \| x_1 - x_2 \|^2) \f$</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">/// with single bandwidth parameter \f$ \gamma \f$.</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// Optionally, the parameter can be encoded as \f$ \exp(\eta) \f$,</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// which allows for unconstrained optimization.</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// \ingroup kernels</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType=RealVector&gt;</div>
<div class="foldopen" id="foldopen00051" data-start="{" data-end="};">
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html">   51</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_gaussian_rbf_kernel.html" title="Gaussian radial basis function kernel.">GaussianRbfKernel</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction</a>&lt;InputType&gt;</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>{</div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">base_type</a>;</div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    </div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    <span class="keyword">struct </span>InternalState: <span class="keyword">public</span> <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a>{</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>        RealMatrix norm2;</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>        RealMatrix expNorm;</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>        </div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>        <span class="keywordtype">void</span> resize(std::size_t sizeX1, std::size_t sizeX2){</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>            norm2.resize(sizeX1, sizeX2);</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>            expNorm.resize(sizeX1, sizeX2);</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>        }</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    };</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a7d064811cbd4b5b0794a52ac6db459df">   66</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#adbf700c2ece7236c70cef4b88777a733" title="batch input type of the kernel">base_type::BatchInputType</a> <a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a7d064811cbd4b5b0794a52ac6db459df">BatchInputType</a>;</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a55e84483f00331c438161ea1e27894cf">   67</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#a40e365cb5ec7d2776105a4aef4e78df3" title="Const references to InputType.">base_type::ConstInputReference</a> <a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a55e84483f00331c438161ea1e27894cf">ConstInputReference</a>;</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a1c968d5375b980746aaf76203b831ee5">   68</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#af923f26f3d015156bb5ac159b302311b" title="Const references to BatchInputType.">base_type::ConstBatchInputReference</a> <a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a1c968d5375b980746aaf76203b831ee5">ConstBatchInputReference</a>;</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span> </div>
<div class="foldopen" id="foldopen00070" data-start="{" data-end="}">
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a9c88ec01375ff1ffbf6b55d812c82107">   70</a></span>    <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a9c88ec01375ff1ffbf6b55d812c82107">GaussianRbfKernel</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#aed0a3d8520b5c08499be99e860f2b2c8" title="Get the bandwidth parameter value.">gamma</a> = 1.0, <span class="keywordtype">bool</span> unconstrained = <span class="keyword">false</span>){</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>        <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a> = <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#aed0a3d8520b5c08499be99e860f2b2c8" title="Get the bandwidth parameter value.">gamma</a>;</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#ab2c897f864da671ebcdaf7051a87cbd1" title="use log storage">m_unconstrained</a> = unconstrained;</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_kernel_function.html#aa13e9ab3b8bbad9e1d773468671703e6">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_kernel_function.html#af54c80ca837961761506e6c2eec15bdead621a9ae065d91a154055a38a7ea72f8" title="is the kernel differentiable w.r.t. its parameters?">base_type::HAS_FIRST_PARAMETER_DERIVATIVE</a>;</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>        this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_kernel_function.html#aa13e9ab3b8bbad9e1d773468671703e6">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_kernel_function.html#af54c80ca837961761506e6c2eec15bdeae4bd575af084f862f64bc665cad4c4ec" title="is the kernel differentiable w.r.t. its inputs?">base_type::HAS_FIRST_INPUT_DERIVATIVE</a>;</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_kernel_function.html#aa13e9ab3b8bbad9e1d773468671703e6">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_kernel_function.html#af54c80ca837961761506e6c2eec15bdea389ad713fc9ba77daf7a89714e5db666" title="does k(x, x) = 1 hold for all inputs x?">base_type::IS_NORMALIZED</a>;</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>    }</div>
</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="comment"></span> </div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00079" data-start="{" data-end="}">
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a916c76c0077640e12e95ffe4a46aa2c8">   79</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a916c76c0077640e12e95ffe4a46aa2c8" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;GaussianRbfKernel&quot;</span>; }</div>
</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span> </div>
<div class="foldopen" id="foldopen00082" data-start="{" data-end="}">
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a10d28ce6e3267a9816007bd6aa5e5ed9">   82</a></span>    RealVector <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a10d28ce6e3267a9816007bd6aa5e5ed9" title="Return the parameter vector.">parameterVector</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        RealVector ret(1);</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        <span class="keywordflow">if</span> (<a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#ab2c897f864da671ebcdaf7051a87cbd1" title="use log storage">m_unconstrained</a>){</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>            ret(0) = std::log(<a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a>); </div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        }</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>        <span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>            ret(0) = <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a>;</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>        }</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>        <span class="keywordflow">return</span> ret;</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>    }</div>
</div>
<div class="foldopen" id="foldopen00092" data-start="{" data-end="}">
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a42aa93855cb79f438c94aed1b910f469">   92</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a42aa93855cb79f438c94aed1b910f469" title="Set the parameter vector.">setParameterVector</a>(RealVector <span class="keyword">const</span>&amp; newParameters){</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(newParameters.size() == 1, <span class="stringliteral">&quot;[GaussianRbfKernel::setParameterVector] invalid size of parameter vector&quot;</span>);</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        <span class="keywordflow">if</span> (<a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#ab2c897f864da671ebcdaf7051a87cbd1" title="use log storage">m_unconstrained</a>){</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>            <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a> = std::exp(newParameters(0));</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>        }</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        <span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>            <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(newParameters(0) &gt; 0.0, <span class="stringliteral">&quot;[GaussianRbfKernel::setParameterVector] gamma must be positive&quot;</span>);</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>            <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a> = newParameters(0);</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        }</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>    }</div>
</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span> </div>
<div class="foldopen" id="foldopen00103" data-start="{" data-end="}">
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#ad7368ea742fb856b0ca846684a18bc3e">  103</a></span>    <span class="keywordtype">size_t</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#ad7368ea742fb856b0ca846684a18bc3e" title="Return the number of parameters.">numberOfParameters</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>        <span class="keywordflow">return</span> 1;</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>    }</div>
</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="comment"></span> </div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="comment">    /// Get the bandwidth parameter value.</span></div>
<div class="foldopen" id="foldopen00108" data-start="{" data-end="}">
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#aed0a3d8520b5c08499be99e860f2b2c8">  108</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#aed0a3d8520b5c08499be99e860f2b2c8" title="Get the bandwidth parameter value.">gamma</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a>;</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>    }</div>
</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment"></span> </div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span><span class="comment">    /// Return ``standard deviation&#39;&#39; of Gaussian.</span></div>
<div class="foldopen" id="foldopen00113" data-start="{" data-end="}">
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#af6df6761901876c48b9318b09ceb2f49">  113</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#af6df6761901876c48b9318b09ceb2f49" title="Return `‘standard deviation’&#39; of Gaussian.">sigma</a>()<span class="keyword"> const</span>{ </div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        <span class="keywordflow">return</span> 1. / std::sqrt(2 * <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a>); </div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>    }</div>
</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span><span class="comment"></span> </div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span><span class="comment">    /// Set the bandwidth parameter value.</span></div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span><span class="comment">    /// \throws shark::Exception if gamma &lt;= 0.</span></div>
<div class="foldopen" id="foldopen00119" data-start="{" data-end="}">
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a352a91e30742cfd02950472f3879eb41">  119</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a352a91e30742cfd02950472f3879eb41">setGamma</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#aed0a3d8520b5c08499be99e860f2b2c8" title="Get the bandwidth parameter value.">gamma</a>){</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(<a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#aed0a3d8520b5c08499be99e860f2b2c8" title="Get the bandwidth parameter value.">gamma</a> &gt; 0.0, <span class="stringliteral">&quot;[GaussianRbfKernel::setGamma] gamma must be positive&quot;</span>);</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a> = <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#aed0a3d8520b5c08499be99e860f2b2c8" title="Get the bandwidth parameter value.">gamma</a>;</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>    }</div>
</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment"></span> </div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment">    /// Set ``standard deviation&#39;&#39; of Gaussian.</span></div>
<div class="foldopen" id="foldopen00125" data-start="{" data-end="}">
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a09f0c248a5fb795f26b1d3295d2d47c7">  125</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a09f0c248a5fb795f26b1d3295d2d47c7" title="Set `‘standard deviation’&#39; of Gaussian.">setSigma</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#af6df6761901876c48b9318b09ceb2f49" title="Return `‘standard deviation’&#39; of Gaussian.">sigma</a>){ </div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a> = 1. / (2 * <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#af6df6761901876c48b9318b09ceb2f49" title="Return `‘standard deviation’&#39; of Gaussian.">sigma</a> * <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#af6df6761901876c48b9318b09ceb2f49" title="Return `‘standard deviation’&#39; of Gaussian.">sigma</a>); </div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>    }</div>
</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment"></span> </div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment">    /// From ISerializable.</span></div>
<div class="foldopen" id="foldopen00130" data-start="{" data-end="}">
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a08353ce4a1575e9dc100e25a71b8643f">  130</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a08353ce4a1575e9dc100e25a71b8643f" title="From ISerializable.">read</a>(<a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a>&amp; ar){</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>        ar &gt;&gt; <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a>;</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        ar &gt;&gt; <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#ab2c897f864da671ebcdaf7051a87cbd1" title="use log storage">m_unconstrained</a>;</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>    }</div>
</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span><span class="comment"></span> </div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span><span class="comment">    /// From ISerializable.</span></div>
<div class="foldopen" id="foldopen00136" data-start="{" data-end="}">
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a4fd68f15eb82ff3894f4077d0cb2d284">  136</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a4fd68f15eb82ff3894f4077d0cb2d284" title="From ISerializable.">write</a>(<a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a>&amp; ar)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        ar &lt;&lt; <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a>;</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        ar &lt;&lt; <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#ab2c897f864da671ebcdaf7051a87cbd1" title="use log storage">m_unconstrained</a>;</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>    }</div>
</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>    <span class="comment"></span></div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span><span class="comment">    ///\brief creates the internal state of the kernel</span></div>
<div class="foldopen" id="foldopen00142" data-start="{" data-end="}">
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a5104097639bdf4b8105c548144e87e50">  142</a></span><span class="comment"></span>    boost::shared_ptr&lt;State&gt; <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a5104097639bdf4b8105c548144e87e50" title="creates the internal state of the kernel">createState</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        <span class="keywordflow">return</span> boost::shared_ptr&lt;State&gt;(<span class="keyword">new</span> InternalState());</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>    }</div>
</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span><span class="comment"></span> </div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span><span class="comment">    /// \brief evaluates \f$ k(x_1,x_2)\f$</span></div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span><span class="comment">    /// Gaussian radial basis function kernel</span></div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span><span class="comment">    /// \f[ k(x_1, x_2) = \exp(-\gamma \cdot \| x_1 - x_2 \|^2) \f]</span></div>
<div class="foldopen" id="foldopen00150" data-start="{" data-end="}">
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a9322aa6046cf102df6fcb2b2f84700aa">  150</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a9322aa6046cf102df6fcb2b2f84700aa" title="evaluates">eval</a>(<a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a55e84483f00331c438161ea1e27894cf">ConstInputReference</a> x1, <a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a55e84483f00331c438161ea1e27894cf">ConstInputReference</a> x2)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(x1.size() == x2.size());</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        <span class="keywordtype">double</span> norm2 = distanceSqr(x2, x1);</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        <span class="keywordtype">double</span> exponential = std::exp(-<a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a> * norm2);</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        <span class="keywordflow">return</span> exponential;</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>    }</div>
</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>    <span class="comment"></span></div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span><span class="comment">    /// \brief evaluates \f$ k(x_1,x_2)\f$ and computes the intermediate value</span></div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span><span class="comment">    /// Gaussian radial basis function kernel</span></div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span><span class="comment">    /// \f[ k(x_1, x_2) = \exp(-\gamma \cdot \| x_1 - x_2 \|^2) \f]</span></div>
<div class="foldopen" id="foldopen00161" data-start="{" data-end="}">
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a055097b043074a5113dc7cb42d506cd9">  161</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a055097b043074a5113dc7cb42d506cd9" title="evaluates  and computes the intermediate value">eval</a>(<a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a1c968d5375b980746aaf76203b831ee5">ConstBatchInputReference</a> batchX1, <a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a1c968d5375b980746aaf76203b831ee5">ConstBatchInputReference</a> batchX2, RealMatrix&amp; result, <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a>&amp; state)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(batchX1.size2() == batchX2.size2());</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        std::size_t sizeX1=batchX1.size1();</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>        std::size_t sizeX2=batchX2.size1();</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        </div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        <span class="comment">//configure state memory</span></div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        InternalState&amp; s=state.<a class="code hl_function" href="structshark_1_1_state.html#a9847e65e063245c6b02371c8b84f8da3" title="Safely downcast State to it&#39;s derived type.">toState</a>&lt;InternalState&gt;();</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>        s.resize(sizeX1,sizeX2);</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span> </div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        <span class="comment">//calculate kernel response</span></div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        noalias(s.norm2)=distanceSqr(batchX1,batchX2);</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        noalias(s.expNorm)=exp(-<a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a>*s.norm2);</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        result=s.expNorm;</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>    }</div>
</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>    </div>
<div class="foldopen" id="foldopen00176" data-start="{" data-end="}">
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a86e172ceab784cc77953e8d9096db0a7">  176</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a86e172ceab784cc77953e8d9096db0a7">eval</a>(<a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a1c968d5375b980746aaf76203b831ee5">ConstBatchInputReference</a> batchX1, <a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a1c968d5375b980746aaf76203b831ee5">ConstBatchInputReference</a> batchX2, RealMatrix&amp; result)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(batchX1.size2() == batchX2.size2());</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        result = distanceSqr(batchX1,batchX2);</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>        noalias(result)=exp(-<a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a>*result);</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>    }</div>
</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>    </div>
<div class="foldopen" id="foldopen00182" data-start="{" data-end="}">
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#ae3fafcbea6b5beb05aa0474f02ae5f3f">  182</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#ae3fafcbea6b5beb05aa0474f02ae5f3f">weightedParameterDerivative</a>(</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        <a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a1c968d5375b980746aaf76203b831ee5">ConstBatchInputReference</a> batchX1, </div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>        <a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a1c968d5375b980746aaf76203b831ee5">ConstBatchInputReference</a> batchX2, </div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>        RealMatrix <span class="keyword">const</span>&amp; coefficients,</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>        <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> <span class="keyword">const</span>&amp; state, </div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>        RealVector&amp; gradient</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>    )<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>        std::size_t sizeX1=batchX1.size1();</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>        std::size_t sizeX2=batchX2.size1();</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>        InternalState <span class="keyword">const</span>&amp; s = state.<a class="code hl_function" href="structshark_1_1_state.html#a9847e65e063245c6b02371c8b84f8da3" title="Safely downcast State to it&#39;s derived type.">toState</a>&lt;InternalState&gt;();</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>        </div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>        <span class="comment">//internal checks</span></div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(batchX1.size2() == batchX2.size2());</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(s.norm2.size1() == sizeX1);</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(s.norm2.size2() == sizeX2);</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(s.expNorm.size1() == sizeX1);</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(s.expNorm.size2() == sizeX2);</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>        </div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>        gradient.resize(1);</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>        gradient(0)= - sum(coefficients *s.expNorm * s.norm2);</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>        <span class="keywordflow">if</span>(<a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#ab2c897f864da671ebcdaf7051a87cbd1" title="use log storage">m_unconstrained</a>){</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>            gradient *= <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a>;</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>        }</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>    }</div>
</div>
<div class="foldopen" id="foldopen00206" data-start="{" data-end="}">
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a31bb55db7ececca83348244ce1d20a78">  206</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#a31bb55db7ececca83348244ce1d20a78">weightedInputDerivative</a>( </div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>        <a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a1c968d5375b980746aaf76203b831ee5">ConstBatchInputReference</a> batchX1, </div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>        <a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a1c968d5375b980746aaf76203b831ee5">ConstBatchInputReference</a> batchX2, </div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>        RealMatrix <span class="keyword">const</span>&amp; coefficientsX2,</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>        <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> <span class="keyword">const</span>&amp; state,</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>        <a class="code hl_typedef" href="classshark_1_1_gaussian_rbf_kernel.html#a7d064811cbd4b5b0794a52ac6db459df">BatchInputType</a>&amp; gradient</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>    )<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>        std::size_t sizeX1=batchX1.size1();</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        std::size_t sizeX2=batchX2.size1();</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>        InternalState <span class="keyword">const</span>&amp; s = state.<a class="code hl_function" href="structshark_1_1_state.html#a9847e65e063245c6b02371c8b84f8da3" title="Safely downcast State to it&#39;s derived type.">toState</a>&lt;InternalState&gt;();</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>        </div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>        <span class="comment">//internal checks</span></div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(batchX1.size2() == batchX2.size2());</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(s.norm2.size1() == sizeX1);</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(s.norm2.size2() == sizeX2);</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(s.expNorm.size1() == sizeX1);</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(s.expNorm.size2() == sizeX2);</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>        </div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>        gradient.resize(sizeX1,batchX1.size2());</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>        RealMatrix W = coefficientsX2*s.expNorm;</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>        noalias(gradient) = prod(W,batchX2);</div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>        RealVector columnSum = sum(as_rows(coefficientsX2*s.expNorm));</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>        </div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != sizeX1; ++i){</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>            noalias(row(gradient,i)) -= columnSum(i) *  row(batchX1,i);</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>        }</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>        gradient*=2.0*<a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a>;</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>    }</div>
</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span>    </div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>    </div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>    <span class="comment">//~ /// \brief Evaluates \f$ \frac {\partial k(x_1,x_2)}{\partial \gamma}\f$ and \f$ \frac {\partial^2 k(x_1,x_2)}{\partial \gamma^2}\f$</span></div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>    <span class="comment">//~ ///</span></div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>    <span class="comment">//~ /// Gaussian radial basis function kernel</span></div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>    <span class="comment">//~ /// \f[ \frac {\partial k(x_1,x_2)}{\partial \gamma} = - \| x_1 - x_2 \|^2 \cdot k(x_1,x_2) \f]</span></div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>    <span class="comment">//~ /// \f[ \frac {\partial^2 k(x_1,x_2)}{\partial^2 \gamma^2} = \| x_1 - x_2 \|^4 \cdot k(x_1,x_2) \f]</span></div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>    <span class="comment">//~ void parameterDerivative(ConstInputReference x1, ConstInputReference x2, Intermediate const&amp; intermediate, RealVector&amp; gradient, RealMatrix&amp; hessian) const{</span></div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>        <span class="comment">//~ SIZE_CHECK(x1.size() == x2.size());</span></div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>        <span class="comment">//~ SIZE_CHECK(intermediate.size() == numberOfIntermediateValues(x1,x2));</span></div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>        <span class="comment">//~ double norm2 = intermediate[0];</span></div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>        <span class="comment">//~ double exponential = intermediate[1];</span></div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span> </div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>        <span class="comment">//~ gradient.resize(1);</span></div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>        <span class="comment">//~ hessian.resize(1, 1);</span></div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>        <span class="comment">//~ if (!m_unconstrained){</span></div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>            <span class="comment">//~ gradient(0) = -exponential * norm2;</span></div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>            <span class="comment">//~ hessian(0, 0) = -gradient(0) * norm2;</span></div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>        <span class="comment">//~ }</span></div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>        <span class="comment">//~ else{</span></div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>            <span class="comment">//~ gradient(0) = -exponential * norm2 * m_gamma;</span></div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>            <span class="comment">//~ hessian(0, 0) = -gradient(0) * norm2 * m_gamma;</span></div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>        <span class="comment">//~ }</span></div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>    <span class="comment">//~ }</span></div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>    <span class="comment">//~ /// \brief Evaluates \f$ \frac {\partial k(x_1,x_2)}{\partial x_1}\f$ and \f$ \frac {\partial^2 k(x_1,x_2)}{\partial x_1^2}\f$</span></div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>    <span class="comment">//~ ///</span></div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>    <span class="comment">//~ /// Gaussian radial basis function kernel</span></div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>    <span class="comment">//~ /// \f[ \frac {\partial k(x_1,x_2)}{\partial x_1} = -2 \gamma \left( x_1 - x_2 \right)\cdot k(x_1,x_2) \f]</span></div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>    <span class="comment">//~ /// \f[ \frac {\partial^2 k(x_1,x_2)}{\partial^2 x_1^2} =2 \gamma \left[ -k(x_1,x_2) \cdot \mathbb{I} - \frac {\partial k(x_1,x_2)}{\partial x_1} ( x_1 - x_2 )^T\right] \f]</span></div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>    <span class="comment">//~ void inputDerivative(const InputType&amp; x1, const InputType&amp; x2, Intermediate const&amp; intermediate, InputType&amp; gradient, InputMatrixType&amp; hessian) const{</span></div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>        <span class="comment">//~ SIZE_CHECK(x1.size() == x2.size());</span></div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>        <span class="comment">//~ SIZE_CHECK(intermediate.size() == numberOfIntermediateValues(x1,x2));</span></div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>        <span class="comment">//~ double exponential = intermediate[1];</span></div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>        <span class="comment">//~ gradient.resize(x1.size());</span></div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>        <span class="comment">//~ noalias(gradient) = (2.0 * m_gamma * exponential) * (x2 - x1);</span></div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>        <span class="comment">//~ hessian.resize(x1.size(), x1.size());</span></div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>        <span class="comment">//~ noalias(hessian) = 2*m_gamma*outer_prod(gradient,x2 - x1)</span></div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>                        <span class="comment">//~ - RealIdentityMatrix(x1.size())*2*m_gamma*exponential;</span></div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>    <span class="comment">//~ }</span></div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span> </div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655">  275</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#a7e4fbf6c367dd4adfddc85c7745cc655" title="kernel bandwidth parameter">m_gamma</a>;         <span class="comment">///&lt; kernel bandwidth parameter</span></div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_rbf_kernel.html#ab2c897f864da671ebcdaf7051a87cbd1">  276</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_gaussian_rbf_kernel.html#ab2c897f864da671ebcdaf7051a87cbd1" title="use log storage">m_unconstrained</a>;           <span class="comment">///&lt; use log storage</span></div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>};</div>
</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span> </div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"><a class="line" href="namespaceshark.html#a09f059e53dac8fd56b4f68c068d0128a">  279</a></span><span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_gaussian_rbf_kernel.html" title="Gaussian radial basis function kernel.">GaussianRbfKernel&lt;&gt;</a> <a class="code hl_typedef" href="namespaceshark.html#a09f059e53dac8fd56b4f68c068d0128a">DenseRbfKernel</a>;</div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno"><a class="line" href="namespaceshark.html#a5f65fb8c716acb1e0c93a138dccfc7bf">  280</a></span><span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_gaussian_rbf_kernel.html" title="Gaussian radial basis function kernel.">GaussianRbfKernel&lt;CompressedRealVector&gt;</a> <a class="code hl_typedef" href="namespaceshark.html#a5f65fb8c716acb1e0c93a138dccfc7bf">CompressedRbfKernel</a>;</div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span> </div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span> </div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span>}</div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span><span class="preprocessor">#endif</span></div>
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